Datasets Used
- Most examples here are based on standard datasets available as R packages
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- Whereever available, we will use data for Sol used for the R Programming tutorial
Recap Example for R Programming Tutorial
## Parsed with column specification:
## cols(
## Type = col_character(),
## Name = col_character(),
## Department = col_character(),
## PI = col_character(),
## PIDept = col_character(),
## Status = col_character(),
## Day = col_date(format = ""),
## SerialJ = col_double(),
## Serial = col_double(),
## SingleJ = col_double(),
## Single = col_double(),
## MultiJ = col_double(),
## Multi = col_double(),
## TotalJ = col_double(),
## Total = col_double()
## )
ay1617su <- c(580320.00,561600.00,580320.00,580320.00,524160.00,580320.00,699840.00,955296.00,924480.00,955296.00,955296.00,924480.00)
Basic Concepts of ggplot2
Grammar of Graphics components:
- Data: Use the
ggplot function to indicate what data to use
- Visual marks: Use
geom_xxx functions to indicate what types of visual marks to use
- Points, lines, area, etc.
- Mapping: Use aesthetic properties (
aes() function) to map variables to visual marks
- Color, shape, size, x, y, etc.
## sex ageYear ageMonth heightIn weightLb
## 1 f 11.92 143 56.3 85.0
## 2 f 12.92 155 62.3 105.0
## 3 f 12.75 153 63.3 108.0
## 4 f 13.42 161 59.0 92.0
## 5 f 15.92 191 62.5 112.5
## 6 f 14.25 171 62.5 112.0
Histogram
## # A tibble: 6 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto(… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manua… f 21 29 p comp…
## 3 audi a4 2 2008 4 manua… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto(… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto(… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manua… f 18 26 p comp…
Bar Charts
monthly <- daily %>%
group_by(Month=floor_date(as.Date(Day), "month"),
Name,Department,PI,PIDept,Status) %>%
summarize(Serial=sum(as.double(Serial)),Single=sum(as.double(Single)),
Multi=sum(as.double(Multi)),Total=sum(as.double(Total)),
SerialJ=sum(as.double(SerialJ)),SingleJ=sum(as.double(SingleJ)),
MultiJ=sum(as.double(MultiJ)),TotalJ=sum(as.double(TotalJ)))
monthly %>%
group_by(Month) %>%
summarize(Total=round(sum(as.double(Total)),2),
Jobs=round(sum(as.double(TotalJ)))) %>%
mutate(Available=ay1617su,Unused=Available-Total,
Percent=round(Total/Available*100,2))
## # A tibble: 12 x 6
## Month Total Jobs Available Unused Percent
## <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016-10-01 309806. 794 580320 270514. 53.4
## 2 2016-11-01 419687. 24315 561600 141913. 74.7
## 3 2016-12-01 467733. 21529 580320 112587. 80.6
## 4 2017-01-01 465625. 7468 580320 114695. 80.2
## 5 2017-02-01 340313. 12382 524160 183847. 64.9
## 6 2017-03-01 370640. 12456 580320 209680. 63.9
## 7 2017-04-01 596186. 7264 699840 103654. 85.2
## 8 2017-05-01 541595. 22755 955296 413701. 56.7
## 9 2017-06-01 568319. 6293 924480 356161. 61.5
## 10 2017-07-01 550470. 20104 955296 404826. 57.6
## 11 2017-08-01 603052. 46016 955296 352244. 63.1
## 12 2017-09-01 551961. 31155 924480 372519. 59.7
Scales
- The
scale_<aes>_(continuous|discrete|manual|identity|...) family of functions controls how data points are mapped to aesthetic values
- Color
- Shape
- Size
- Alpha (transparency)
- X and Y location
- If X or Y is a continuous variable, then you can scale it using
scale_(x|y)_(continous|log10)
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
By default:
Re-scaled
Data Visualization in Python
plt.plot([1,2,3,4], [1,4,9,16], 'ro')
plt.axis([0, 6, 0, 20])
plt.show()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot([1, 2, 3, 4], [10, 20, 25, 30], color='lightblue', linewidth=3)
ax.scatter([0.3, 3.8, 1.2, 2.5], [11, 25, 9, 26], color='darkgreen', marker='^')
ax.set_xlim(0.5, 4.5)
plt.show()
plt.plot([1,2,3,4], [1,4,9,16], 'ro')
plt.axis([0, 6, 0, 20])
plt.show()
t = np.arange(0., 5., 0.2)
# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()
def f(t):
return np.exp(-t) * np.cos(2*np.pi*t)
t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)
plt.figure(1)
plt.subplot(211)
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
plt.subplot(212)
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
plt.show()
## /Users/apacheco/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning:
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## /Users/apacheco/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning:
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## alternative="'density'", removal="3.1")
N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
menStd = (2, 3, 4, 1, 2)
womenStd = (3, 5, 2, 3, 3)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
p1 = plt.bar(ind, menMeans, width, yerr=menStd)
p2 = plt.bar(ind, womenMeans, width,
bottom=menMeans, yerr=womenStd)
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('Men', 'Women'))
plt.show()
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
import pandas
import matplotlib.pyplot as plt
monthly = pandas.read_csv('solmonthly-public.csv')
labels = np.array(monthly['Month'])
ax = monthly[['Month','Percent']].plot(kind='bar', title ="Sol Usage", figsize=(8,6), legend=True, fontsize=12)
ax.set_xlabel("Month", fontsize=12)
ax.set_ylabel("Percent", fontsize=12)
ax.set_xticklabels(labels, rotation=20, horizontalalignment = 'right')
plt.show()
